Data Governance in the Age of GenAI: Who Owns the Output?

When artificial intelligence advanced from merely analysing data to creating it, digital governance changed fundamentally. Previously, managing databases, permissions, and lineage was key. Now, organisations must govern systems that learn, infer, and generate original content at scale. GenAI makes data a strategic asset, so robust governance frameworks are essential for trust, compliance, and scalability.

It is no longer just an operational artefact. It is a strategic asset and often serves as the foundation of a competitive advantage. While 97% of business leaders view GenAI as a transformative technology, nearly half (48%) say their organisations lack enough high-quality data to make it worthwhile. This gap highlights a problem: past governance models for structured systems do not meet the needs of generative intelligence. As we examine the evolution from traditional data stewardship to GenAI-enabled processes, it’s clear that a new mindset is required.

From Systems of Record to Systems of Generation:

Historically, governance focused on ‘systems of record’, static stores of truth where we tracked access, modification, and flow. GenAI shifts this focus to ‘systems of generation’, where models continuously ingest, transform, and create data, effectively blurring the lines between input, model, prompt, and output.

In this new architecture, lineage extends beyond data flow to model provenance. Governance must now track which model version was used, the fine-tuning datasets involved, and the prompt history. Without this visibility, organisations risk ‘Shadow AI’, unsanctioned use of GenAI tools that creates critical security blind spots. A report by Palo Alto Networks noted that Data Loss Prevention (DLP) incidents related to GenAI have more than doubled recently, underscoring the severity of the risk.

Furthermore, the ‘quality imperative’ has never been more urgent. GenAI models are only as good as the data they consume, and poorly governed or inconsistent training data is the root cause of AI bias and ‘hallucinations’. Gartner found that 60% of organisations fail to capture the full value of their AI roadmaps, specifically due to inadequate data governance.

The Ownership Riddle: A Legal Grey Area

As models generate code, research insights, and creative work, a new dilemma emerges: who owns the output? Traditional Intellectual Property (IP) laws, founded on the concept of human originality, are often ill-equipped to address machine-generated works. In most jurisdictions, including India and the U.S., works created solely by AI generally lack the ‘human author’ required for copyright protection.
This regulatory vacuum leaves ownership open to three potential claimants, creating significant legal uncertainty:

The user: The person entering the prompt. However, the U.S. Copyright Office has clarified that short prompts rarely meet the ‘meaningful human input’ requirement for copyright protection.

The model developer: Companies like OpenAI or Google. While OpenAI assigns output rights to users, other providers may vary in their approach.

The data owner: The entity owning the original material used to train the model.

Currently, ownership is determined more by contract than by law. However, complexities arise from training data copyrights; if your fine-tuning set includes licensed content, your right to commercialize outputs may be limited by that license, regardless of the model provider's terms. Forrester predicts ongoing IP disputes over copyrighted material used in models, making this a board-level concern.

The Path Forward: AI-Ready Governance

To navigate these risks, technology leaders must adopt an ‘AI-ready’ governance approach, where automation and metadata help embed policy into day-to-day workflows. This requires a shift from deterministic data quality rules to ‘guardrails’ for probabilistic systems, such as prompt filters, toxicity checks, and PII redaction.

The future state of governance will likely rely on GenAI data contracts. Similar to API contracts, these are machine-readable policies that define what can be used in prompts or retrieved into context windows. These contracts will encode PII rules, IP licenses, and geo-fencing, evaluating them automatically at runtime to ensure compliance.

Furthermore, we can expect a convergence of data governance, MLOps, and Model Risk Management into a unified control plane. As regulations like the EU AI Act and India’s Digital Personal Data Protection (DPDP) Act tighten expectations for consent and cross-border governance, standardised attestations for AI systems, covering provenance, bias metrics, and hallucination rates, will become a procurement requirement.

In this model, governance is not an afterthought; it is the operating system. By establishing clear lineage and treating datasets and models as products, organisations can mitigate liability and ensure that GenAI scales safely.

Conclusion

GenAI doesn’t just generate content; it creates assets that redefine ownership, traceability, and accountability. As regulations tighten and AI’s power accelerates, governance becomes the crucial factor that will distinguish industry leaders from laggards. Those who confront the ownership issue now will not only leverage AI, they will command it. Ownership will decide the ultimate winners in the age of generative intelligence.

Bharat Chadha

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Bharat Chadha, Partner, Tech Consulting Practice, Uniqus Consultech